Phase 4: Training LLMs · ~45 minutes · Python
Open Models: Architecture Walkthroughs
You built a GPT-2 Small from scratch in Lesson 04. Frontier open models in 2026 are the same family with five or six concrete changes. RMSNorm instead of LayerNorm. SwiGLU instead of GELU. RoPE instead of learned positions. GQA or MLA instead of full MHA. Mixture-of-Experts at scale. The math you already know covers 95% of them. This lesson reads Llama 3, DeepSeek-V3, Mixtral, Qwen, and Gemma side by side and names the exact line where each architecture diverges.
What you will learn
- Read the config.json of Llama 3, Mistral, Mixtral, Gemma 2, Qwen 2.5, and DeepSeek-V3 and explain every field
- Name the specific architectural change each model made versus GPT-2 Small and justify it from first principles
- Compute parameter count, KV cache size, and activation memory for any open model from its config alone
- Pick the right open model for a deployment target given latency, memory, and capability constraints
Introduction
- Type: Learn
- Languages: Python (stdlib)
- Prerequisites: Pre-Training a Mini GPT (124M Parameters), Scaling Laws, KV Cache, Flash Attention & Inference Optimization
- Time: ~45 minutes
Objective
Learning objectives
- Read the config.json of Llama 3, Mistral, Mixtral, Gemma 2, Qwen 2.5, and DeepSeek-V3 and explain every field
- Name the specific architectural change each model made versus GPT-2 Small and justify it from first principles
- Compute parameter count, KV cache size, and activation memory for any open model from its config alone
- Pick the right open model for a deployment target given latency, memory, and capability constraints
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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